OpenRaiser/Intern-Atlas huggingface dataset:
Monitoring with uncertainty
Venue: arXiv cs.LO 2013
Year: 2013

We discuss the problem of runtime verification of an instrumented program that misses to emit and to monitor some events. These gaps can occur when a monitoring overhead control mechanism is introduced to disable the monitor of an application with real-time constraints. We show how to use statistical models to learn the application behavior and to "fill in" the introduced gaps. Finally, we present and discuss some techniques developed in the last three years to estimate the probability that a property of interest is violated in the presence of an incomplete trace.
Combinatorial Abstractions of Dynamical Systems
Venue: arXiv cs.SY 2013
Year: 2013

Formal verification has been successfully developed in computer science for verifying combinatorial classes of models and specifications. In like manner, formal verification methods have been developed for dynamical systems. However, the verification of system properties, such as safety, is based on reachability calculations, which are the sources of insurmountable complexity. This talk addresses indirect verification methods, which are based on abstracting the dynamical systems by models of reduced complexity and preserving central properties of the original systems.
A stochastic hybrid model of a biological filter
Venue: arXiv cs.LG 2013
Year: 2013

We present a hybrid model of a biological filter, a genetic circuit which removes fast fluctuations in the cell's internal representation of the extra cellular environment. The model takes the classic feed-forward loop (FFL) motif and represents it as a network of continuous protein concentrations and binary, unobserved gene promoter states. We address the problem of statistical inference and parameter learning for this class of models from partial, discrete time observations. We show that the hybrid representation leads to an efficient algorithm for approximate statistical inference in this circuit, and show its effectiveness on a simulated data set.
Measuring Privacy Leakage for IDS Rules
Venue: arXiv cs.CR 2013
Year: 2013

This paper proposes a measurement approach for estimating the privacy leakage from Intrusion Detection System (IDS) alarms. Quantitative information flow analysis is used to build a theoretical model of privacy leakage from IDS rules, based on information entropy. This theoretical model is subsequently verified empirically both based on simulations and in an experimental study. The analysis shows that the metric is able to distinguish between IDS rules that have no or low expected privacy leakage and IDS rules with a significant risk of leaking sensitive information, for example on user behaviour. The analysis is based on measurements of number of IDS alarms, data length and data entropy for relevant parts of IDS rules (for example payload). This is a promising approach that opens up for privacy benchmarking of Managed Security Service providers.
Flaglets for studying the large-scale structure of the Universe
Venue: arXiv cs.IT 2013
Year: 2013

Pressing questions in cosmology such as the nature of dark matter and dark energy can be addressed using large galaxy surveys, which measure the positions, properties and redshifts of galaxies in order to map the large-scale structure of the Universe. We review the Fourier-Laguerre transform, a novel transform in 3D spherical coordinates which is based on spherical harmonics combined with damped Laguerre polynomials and appropriate for analysing galaxy surveys. We also recall the construction of flaglets, 3D wavelets obtained through a tiling of the Fourier-Laguerre space, which can be used to extract scale-dependent, spatially localised features on the ball. We exploit a sampling theorem to obtain exact Fourier-Laguerre and flaglet transforms, such that band-limited signals can analysed and reconstructed at floating point accuracy on a finite number of voxels on the ball. We present a potential application of the flaglet transform for finding voids in galaxy surveys and studying the large-scale structure of the Universe.
Post-processing of Engineering Analysis Results for Visualization in VR Systems
Venue: arXiv cs.GR 2013
Year: 2013

The applicability of Virtual Reality for evaluating engineering analysis results is beginning to receive increased appreciation in the last years. The problem many engineers are still facing is how to import their model together with the analysis results in a virtual reality environment for exploration and results validation. In this paper we propose an algorithm for transforming model data and results from finite element analysis (FEA) solving application to a format easily interpretable by a virtual reality application. The algorithm includes also steps for reducing the face-count of the resulting mesh by eliminating faces from the inner part of the model in the cases when only the surfaces of the model is analyzed. We also describe a possibility for simultaneously assessing multiple analysis results relying on multimodal results presentation by stimulating different senses of the operator.
On the weight distributions of several classes of cyclic codes from APN monomials
Venue: arXiv cs.IT 2013
Year: 2013

Let $m\geq 3$ be an odd integer and $p$ be an odd prime. % with $p-1=2^rh$, where $h$ is an odd integer. In this paper, many classes of three-weight cyclic codes over $\mathbb{F}_{p}$ are presented via an examination of the condition for the cyclic codes $\mathcal{C}_{(1,d)}$ and $\mathcal{C}_{(1,e)}$, which have parity-check polynomials $m_1(x)m_d(x)$ and $m_1(x)m_e(x)$ respectively, to have the same weight distribution, where $m_i(x)$ is the minimal polynomial of $π^{-i}$ over $\mathbb{F}_{p}$ for a primitive element $π$ of $\mathbb{F}_{p^m}$. %For $p=3$, the duals of five classes of the proposed cyclic codes are optimal in the sense that they meet certain bounds on linear codes. Furthermore, for $p\equiv 3 \pmod{4}$ and positive integers $e$ such that there exist integers $k$ with $\gcd(m,k)=1$ and $τ\in\{0,1,\cdots, m-1\}$ satisfying $(p^k+1)\cdot e\equiv 2 p^τ\pmod{p^m-1}$, the value distributions of the two exponential sums $T(a,b)=\sum\limits_{x\in \mathbb{F}_{p^m}}ω^{\Tr(ax+bx^e)}$ and $ S(a,b,c)=\sum\limits_{x\in \mathbb{F}_{p^m}}ω^{\Tr(ax+bx^e+cx^s)}, $ where $s=(p^m-1)/2$, are settled. As an application, the value distribution of $S(a,b,c)$ is utilized to investigate the weight distribution of the cyclic codes $\mathcal{C}_{(1,e,s)}$ with parity-check polynomial $m_1(x)m_e(x)m_s(x)$. In the case of $p=3$ and even $e$ satisfying the above condition, the duals of the cyclic codes $\mathcal{C}_{(1,e,s)}$ have the optimal minimum distance.
Biological effects and equivalent doses in radiotherapy: a software solution
Venue: arXiv cs.CE 2013
Year: 2013

The limits of TDF (time, dose, and fractionation) and linear quadratic models have been known for a long time. Medical physicists and physicians are required to provide fast and reliable interpretations regarding the delivered doses or any future prescriptions relating to treatment changes. We therefore propose a calculation interface under the GNU license to be used for equivalent doses, biological doses, and normal tumor complication probability (Lyman model). The methodology used draws from several sources: the linear-quadratic-linear model of Astrahan, the repopulation effects of Dale, and the prediction of multi-fractionated treatments of Thames. The results are obtained from an algorithm that minimizes an ad-hoc cost function, and then compared to the equivalent dose computed using standard calculators in seven French radiotherapy centers.
Low-rank approximation in the numerical modeling of the Farley-Buneman instability in ionospheric plasma
Venue: arXiv cs.NA 2013
Year: 2013

We consider the numerical modeling of the Farley-Buneman instability development in the earth's ionosphere plasma. The ion behavior is governed by the kinetic Landau equation in the four-dimensional phase space, and since the finite difference discretization on a tensor product grid is used, this equation becomes the most computationally challenging part of the scheme. To relax the complexity and memory consumption, an adaptive model reduction using the low-rank separation of variables, namely the Tensor Train format, is employed. The approach was verified via the prototype MATLAB implementation. Numerical experiments demonstrate the possibility of efficient separation of space and velocity variables, resulting in the solution storage reduction by a factor of order tens.
Data Grid Concepts for Data Security in Distributed Computing
Venue: arXiv cs.DC 2013
Year: 2013

Data grid is a distributed computing architecture that integrates a large number of data and computing resources into a single virtual data management system. It enables the sharing and coordinated use of data from various resources and provides various services to fit the needs of high performance distributed and data-intensive computing. Here data partitioning and dynamic replication in data grid are considered. In which security and access performance of a system are efficient. There are several important requirements for data grids, including information survivability, security, and access performance. More specifically, the investigation is the problem of optimal allocation of sensitive data objects that are partitioned by using secret sharing scheme or erasure coding scheme and replicated. DATA PARTITIONING is known as the single data can be divided into multiple objects. REPLICATION is known as process of sharing information. storing same data in multiple systems. Replication techniques are frequently used to improve data availability. Single point failure does not affect this system. Where the data will be secured.
Micro Spitbol
Venue: arXiv cs.PL 2013
Year: 2013

A compact version of MACRO SPITBOL, a compiler/ interpreter for a variant of SNOBOL4, has been developed for use on microcomputer systems. The techniques for producing an implementation are largely automatic in order to preserve the integrity and portability of the SPITBOL system. These techniques are discussed along with a description of an initial implementation on a 65K byte minicomputer. An interesting theoretical problem which arises when using procedures which compact the interpretive object code is also analyzed.
DISCO: Distributed Multi-domain SDN Controllers
Venue: arXiv cs.NI 2013
Year: 2013

Modern multi-domain networks now span over datacenter networks, enterprise networks, customer sites and mobile entities. Such networks are critical and, thus, must be resilient, scalable and easily extensible. The emergence of Software-Defined Networking (SDN) protocols, which enables to decouple the data plane from the control plane and dynamically program the network, opens up new ways to architect such networks. In this paper, we propose DISCO, an open and extensible DIstributed SDN COntrol plane able to cope with the distributed and heterogeneous nature of modern overlay networks and wide area networks. DISCO controllers manage their own network domain and communicate with each others to provide end-to-end network services. This communication is based on a unique lightweight and highly manageable control channel used by agents to self-adaptively share aggregated network-wide information. We implemented DISCO on top of the Floodlight OpenFlow controller and the AMQP protocol. We demonstrated how DISCO's control plane dynamically adapts to heterogeneous network topologies while being resilient enough to survive to disruptions and attacks and providing classic functionalities such as end-point migration and network-wide traffic engineering. The experimentation results we present are organized around three use cases: inter-domain topology disruption, end-to-end priority service request and virtual machine migration.
Bayesian Conditional Gaussian Network Classifiers with Applications to Mass Spectra Classification
Venue: arXiv cs.LG 2013
Year: 2013

Classifiers based on probabilistic graphical models are very effective. In continuous domains, maximum likelihood is usually used to assess the predictions of those classifiers. When data is scarce, this can easily lead to overfitting. In any probabilistic setting, Bayesian averaging (BA) provides theoretically optimal predictions and is known to be robust to overfitting. In this work we introduce Bayesian Conditional Gaussian Network Classifiers, which efficiently perform exact Bayesian averaging over the parameters. We evaluate the proposed classifiers against the maximum likelihood alternatives proposed so far over standard UCI datasets, concluding that performing BA improves the quality of the assessed probabilities (conditional log likelihood) whilst maintaining the error rate. Overfitting is more likely to occur in domains where the number of data items is small and the number of variables is large. These two conditions are met in the realm of bioinformatics, where the early diagnosis of cancer from mass spectra is a relevant task. We provide an application of our classification framework to that problem, comparing it with the standard maximum likelihood alternative, where the improvement of quality in the assessed probabilities is confirmed.
The Shapley Axiomatization for Values in Partition Function Games
Venue: arXiv cs.GT 2013
Year: 2013

One of the long-debated issues in coalitional game theory is how to extend the Shapley value to games with externalities (partition-function games). When externalities are present, not only can a player's marginal contribution - a central notion to the Shapley value - be defined in a variety of ways, but it is also not obvious which axiomatization should be used. Consequently, a number of authors extended the Shapley value using complex and often unintuitive axiomatizations. Furthermore, no algorithm to approximate any extension of the Shapley value to partition-function games has been proposed to date. Given this background, we prove in this paper that, for any well-defined measure of marginal contribution, Shapley's original four axioms imply a unique value for games with externalities. As an consequence of this general theorem, we show that values proposed by Macho-Stadler et al., McQuillin and Bolger can be derived from Shapley's axioms. Building upon our analysis of marginal contribution, we develop a general algorithm to approximate extensions of the Shapley value to games with externalities using a Monte Carlo simulation technique.
Development of a language and its enacting engine for the unified discovery of heterogeneous services
Venue: arXiv cs.SE 2013
Year: 2013

Service orientation fosters a high-level model for distributed applications development, which is based on the discovery, composition and reuse of existing software services. However, the heterogeneity among current service-oriented technologies renders the important task of service discovery tedious and ineffective. This dissertation proposes a new approach to address this challenge. Specifically, it contributes a framework supporting the unified discovery of heterogeneous services, with a focus on web, peer-to-peer, and grid services. The framework comprises a service query language and its enacting service discovery engine. Overall, the proposed solution is characterized by generality and flexibility, which are ensured by appropriate abstractions, extension points, and their sup- porting mechanisms. The viability, performance, and effectiveness of the proposed framework are demonstrated by experimental measurements.
Learning-Based Procedural Content Generation
Venue: arXiv cs.AI 2013
Year: 2013

Procedural content generation (PCG) has recently become one of the hottest topics in computational intelligence and AI game researches. Among a variety of PCG techniques, search-based approaches overwhelmingly dominate PCG development at present. While SBPCG leads to promising results and successful applications, it poses a number of challenges ranging from representation to evaluation of the content being generated. In this paper, we present an alternative yet generic PCG framework, named learning-based procedure content generation (LBPCG), to provide potential solutions to several challenging problems in existing PCG techniques. By exploring and exploiting information gained in game development and public beta test via data-driven learning, our framework can generate robust content adaptable to end-user or target players on-line with minimal interruption to their experience. Furthermore, we develop enabling techniques to implement the various models required in our framework. For a proof of concept, we have developed a prototype based on the classic open source first-person shooter game, Quake. Simulation results suggest that our framework is promising in generating quality content.
Branch Cuts in Maple 17
Venue: arXiv cs.SC 2013
Year: 2013

Accurate and comprehensible knowledge about the position of branch cuts is essential for correctly working with multi-valued functions, such as the square root and logarithm. We discuss the new tools in Maple 17 for calculating and visualising the branch cuts of such functions, and others built up from them. The cuts are described in an intuitive and accurate form, offering substantial improvement on the descriptions previously available.
Classification and construction of closed-form kernels for signal representation on the 2-sphere
Venue: arXiv cs.IT 2013
Year: 2013

This paper considers the construction of Reproducing Kernel Hilbert Spaces (RKHS) on the sphere as an alternative to the conventional Hilbert space using the inner product that yields the L^2(S^2) function space of finite energy signals. In comparison with wavelet representations, which have multi-resolution properties on L^2(S^2), the representations that arise from the RKHS approach, which uses different inner products, have an overall smoothness constraint, which may offer advantages and simplifications in certain contexts. The key contribution of this paper is to construct classes of closed-form kernels, such as one based on the von Mises-Fisher distribution, which permits efficient inner product computation using kernel evaluations. Three classes of RKHS are defined: isotropic kernels and non-isotropic kernels both with spherical harmonic eigenfunctions, and general anisotropic kernels.
Local Average Consensus in Distributed Measurement of Spatial-Temporal Varying Parameters: 1D Case
Venue: arXiv cs.SY 2013
Year: 2013

We study a new variant of consensus problems, termed `local average consensus', in networks of agents. We consider the task of using sensor networks to perform distributed measurement of a parameter which has both spatial (in this paper 1D) and temporal variations. Our idea is to maintain potentially useful local information regarding spatial variation, as contrasted with reaching a single, global consensus, as well as to mitigate the effect of measurement errors. We employ two schemes for computation of local average consensus: exponential weighting and uniform finite window. In both schemes, we design local average consensus algorithms to address first the case where the measured parameter has spatial variation but is constant in time, and then the case where the measured parameter has both spatial and temporal variations. Our designed algorithms are distributed, in that information is exchanged only among neighbors. Moreover, we analyze both spatial and temporal frequency responses and noise propagation associated with the algorithms. The tradeoffs of using local consensus, as compared to standard global consensus, include higher memory requirement and degraded noise performance. Arbitrary updating weights and random spacing between sensors are analyzed in the proposed algorithms.
DorFin: WiFi Fingerprint-based Localization Revisited
Venue: arXiv cs.NI 2013
Year: 2013

Although WiFi fingerprint-based indoor localization is attractive, its accuracy remains a primary challenge especially in mobile environments. Existing approaches either appeal to physical layer information or rely on extra wireless signals for high accuracy. In this paper, we revisit the RSS fingerprint-based localization scheme and reveal crucial observations that act as the root causes of localization errors, yet are surprisingly overlooked or even unseen in previous works. Specifically, we recognize APs' diverse discrimination for fingerprinting a specific location, observe the RSS inconsistency caused by signal fluctuations and human body blockages, and uncover the RSS outdated problem on commodity smartphones. Inspired by these insights, we devise a discrimination factor to quantify different APs' discrimination, incorporate robust regression to tolerate outlier measurements, and reassemble different fingerprints to cope with outdated RSSs. Combining these techniques in a unified solution, we propose DorFin, a novel scheme of fingerprint generation, representation, and matching, which yields remarkable accuracy without incurring extra cost. Extensive experiments demonstrate that DorFin achieves mean error of 2 meters and more importantly, bounds the 95th percentile error under 5.5 meters; these are about 56% and 69% lower, respectively, compared with the state-of-the-art schemes such as Horus and RADAR.
Distributed H-infinity Tracking Control for Discrete-Time Multi-Agent Systems with a High-Dimensional Leader
Venue: arXiv cs.SY 2013
Year: 2013

This paper considers the distributed H-infinity leader-following tracking problem for a class of discrete time multi-agent systems with a high-dimensional dynamic leader. It is assumed that output information about the leader is only available to designated followers, and the dynamics of the followers are subject to perturbations. To achieve distributed H-infinity leader-following tracking, a new class of control protocols is proposed which is based on the feedback from the nearest neighbors as well as a distributed state estimator. Under the assumptions that dynamics of the leader are detectable and the communication topology contains a directed spanning tree, sufficient conditions are obtained that enable all followers to track the leader while achieving a desired H-infinity leader-following tracking performance. Numerical simulations illustrate the effectiveness of the theoretical analysis.
Preventing Disclosure of Sensitive Knowledge by Hiding Inference
Venue: arXiv cs.CR 2013
Year: 2013

Data Mining is a way of extracting data or uncovering hidden patterns of information from databases. So, there is a need to prevent the inference rules from being disclosed such that the more secure data sets cannot be identified from non sensitive attributes. This can be done through removing or adding certain item sets in the transactions Sanitization. The purpose is to hide the Inference rules, so that the user may not be able to discover any valuable information from other non sensitive data and any organisation can release all samples of their data without the fear of Knowledge Discovery In Databases which can be achieved by investigating frequently occurring item sets, rules that can be mined from them with the objective of hiding them. Another way is to release only limited samples in the new database so that there is no information loss and it also satisfies the legitimate needs of the users. The major problem is uncovering hidden patterns, which causes a threat to the database security. Sensitive data are inferred from non-sensitive data based on the semantics of the application the user has, commonly known as the inference problem. Two fundamental approaches to protect sensitive rules from disclosure are that, preventing rules from being generated by hiding the frequent sets of data items and reducing the importance of the rules by setting their confidence below a user-specified threshold.